LET- Life Engagement Test (Scheier et al., 2006)
Repeated Measures for Life Engagement, w/ all questions (reverse coded)
#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
# Load the psych package
library(psych)Creating a new variable that is the mean of all positive purpose meanLET questions
items <- c("LET1", "LET2", "LET3", "LET4", "LET5", "LET6")
scaleKey <- c(-1, 1, -1,1,-1,1)
data.test4$meanLET <- scoreItems(scaleKey, items=data.test4[,items], delete=FALSE)$score
library(reshape2); library(car)## Warning: package 'car' was built under R version 3.1.2##
## Attaching package: 'car'
##
## The following object is masked from 'package:psych':
##
## logitdata <- data.test4[,c("ID", "GROUP", "wave", "meanLET")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanLET")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )Create new data set with ID Group baseline meanLET and wave so that we have Baseline, time 1 and 2 to compare to
data2 <- as.data.frame(mapply(c,data[,1:4], data[,c(1:3,5)]))
data2$wave <- rep(1:2, each=89)
names(data2) <- c("ID", "GROUP", "BASELINE", "meanLET", "WAVE")Drop the cases where participants did not complete the intervention completely
#data2 <- data2[-c(which(data2$GROUP ==2)),]Intention to treat model (ITT) where we keep the cases who dropped out and did not complete the study (http://en.wikipedia.org/wiki/Intention-to-treat_analysis).
data2[which(data2$GROUP ==2), "GROUP"] <- 1For lme to work GROUP and ID need to be seen as factors
data2$GROUP <-as.factor(data2$GROUP)
data2$ID <-as.factor(data2$ID)Describe the meanLET variable by the GROUP variable
describeBy(data2[,3:4], group = data2$GROUP)## group: 0
## vars n mean sd median trimmed mad min max range skew
## BASELINE 1 86 3.82 0.70 3.83 3.83 0.74 2.50 5 2.50 -0.23
## meanLET 2 59 4.05 0.81 4.17 4.14 0.74 1.83 5 3.17 -0.86
## kurtosis se
## BASELINE -0.90 0.08
## meanLET -0.03 0.11
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew
## BASELINE 1 88 3.59 0.86 3.58 3.60 0.86 2.00 5 3.00 -0.09
## meanLET 2 54 4.20 0.72 4.25 4.29 0.74 2.17 5 2.83 -0.98
## kurtosis se
## BASELINE -0.98 0.09
## meanLET 0.49 0.10Create a plot that visualizes meanLET variable by the GROUP variable
library(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%Take a look at the residuals
residual <- lm(meanLET ~ BASELINE, data=data2)$residualPlot the residuals to see that they are random
plot(density(residual))# A density plot
qqnorm(residual) # A quantile normal plot to checking normality
qqline(residual)
Checking the different between intervention and control groups residuals. This allows us to control for individual unsystematic differences.
data2$residual <- NA
sel1 <- which(!is.na(data2$meanLET))
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, meanLET, data=data2, geom="boxplot")## Warning: Removed 65 rows containing non-finite values (stat_boxplot).
Plot of the difference between intervention and control groups.
qplot(GROUP, residual, data=data2, geom="boxplot")## Warning: Removed 69 rows containing non-finite values (stat_boxplot).
Two way repeated measures ======================================================== Graphing the Two-Way Interaction. Both meanLET and the Residuals
# Load the nlme package
library(nlme)
with(data2, boxplot(meanLET ~ WAVE + GROUP))
with(data2, boxplot(residual ~ WAVE + GROUP))
Comparing Basline to Wave 2 and 3 by Group.
fullModel <- lme(meanLET ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = data2, method = "ML", na.action = "na.omit")Explanation of significance:
We asses the significance of our models by comparing them from the baseline model using the anova() function.
(Intercept): Where everything is 0
GROUP1: Is there a difference between group. If it is significant than there is a difference and the treatment had an effect.
WAVE: Asseses whether the effects gets bigger beteen time 2 and 3 (does not have to be significant)
BASELINE: Should not be significant. If it is then it shows that there is a difference between groups before the treatment.
GROUP1:WAVE: If this is significant then it means that the effect was either fleeting or it happened after the treatment i.e. between time 2 and 3.
summary(fullModel)## Linear mixed-effects model fit by maximum likelihood
## Data: data2
## AIC BIC logLik
## 168.1 186.9 -77.03
##
## Random effects:
## Formula: ~1 | ID
## (Intercept) Residual
## StdDev: 0.376 0.3594
##
## Fixed effects: meanLET ~ GROUP * WAVE + BASELINE
## Value Std.Error DF t-value p-value
## (Intercept) 1.2053 0.3373 66 3.573 0.0007
## GROUP1 -0.0244 0.2443 66 -0.100 0.9209
## WAVE -0.0946 0.1054 38 -0.897 0.3754
## BASELINE 0.7536 0.0773 66 9.755 0.0000
## GROUP1:WAVE 0.2525 0.1540 38 1.639 0.1094
## Correlation:
## (Intr) GROUP1 WAVE BASELI
## GROUP1 -0.384
## WAVE -0.387 0.590
## BASELINE -0.871 0.060 -0.048
## GROUP1:WAVE 0.273 -0.871 -0.684 0.024
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -3.33870 -0.39783 0.03128 0.43784 1.96358
##
## Number of Observations: 109
## Number of Groups: 69Table with P-values
| Value | Std.Error | DF | t-value | p-value | |
|---|---|---|---|---|---|
| (Intercept) | 1.2053 | 0.3373 | 66.0000 | 3.5730 | 0.0007 |
| GROUP1 | -0.0244 | 0.2443 | 66.0000 | -0.0997 | 0.9209 |
| WAVE | -0.0946 | 0.1054 | 38.0000 | -0.8969 | 0.3754 |
| BASELINE | 0.7536 | 0.0773 | 66.0000 | 9.7546 | 0.0000 |
| GROUP1:WAVE | 0.2525 | 0.1540 | 38.0000 | 1.6393 | 0.1094 |
``` Table with confidence intervals
| est. | lower | upper | |
|---|---|---|---|
| (Intercept) | 1.2053 | 0.5474 | 1.8632 |
| GROUP1 | -0.0244 | -0.5009 | 0.4522 |
| WAVE | -0.0946 | -0.3030 | 0.1139 |
| BASELINE | 0.7536 | 0.6029 | 0.9042 |
| GROUP1:WAVE | 0.2525 | -0.0521 | 0.5571 |